Optimal Planning of Photovoltaic Distributed Generation Considering Time-Varying Loads

The integration of photovoltaic distributed generation (PVDG) into power systems has gained significant attention due to its potential for renewable energy generation and the reduction of greenhouse gas emissions. However, the intermittent nature of solar power and the presence of time-varying loads pose challenges to the optimal planning and utilization of PV systems. This research focuses on addressing the optimal planning of PVDG considering time-varying loads. The backward/forward sweep power flow (BFSPF) with mix-integer optimization by genetic algorithm (MIOGA) methods are used to optimally size and locate PVDGs in the radial distribution network (RDN) while considering the dynamic nature of loads over time. There are three time-varying load cases: residential, commercial, and industrial. In MATLAB, the approach is evaluated using a conventional 33-bus RDN. With the installation of PVDG, the simulation results show a reduction in total power loss and an improvement in voltage magnitudes for the network. According to the findings, multi-PVDG installation in the residential, commercial, and industrial load models can minimize power losses by up to 58.96%, 54.49%, and 56.92%, respectively. Aside from lowering losses, installing PVDG also helps to enhance the voltage profile of the radial distribution network. The findings highlight the importance of considering load fluctuations to achieve optimal integration of PVDG into power distribution networks, ultimately contributing to the transition towards a sustainable energy future.